The main goal of radiotherapy planning (RTP) is to determine fast and accurately the best dose distribution (i.e., fluence beam profile) which can satisfy as much as possible all clinical goals. For instance, a certain dose must be delivered to the tumor, sparing as much as possible nearby organs at risk (OARs). Therefore, optimization problems in radiotherapy (RT) inverse planning are inherently multi-criteria problems since they involve multiple planning goals for tumor targets and neighboring critical tissue structures. Clinical decisions are generally required, commonly based on an assignment of importance weights among these conflicting goals until the clinical wishes appear satisfied. Satisfying the clinical wishes typically involves many repetitive optimizations. Recently, in treatment planning systems (TPS) such as, e.g., the Philips Pinnacle3 treatment planning system, “auto-planning” routines have been included to automatically generate plans which can satisfy clinical requirements, see the article “MO-D-BRB-07: Automated IMRT Plan Generation for Prostate Cancer”, Med. Phys. (2010), Vol. 37, pp. 3340-3340 by R. Kashani et al., which is incorporated herein by reference. The implementation of these auto-planning routines relies on ‘scripts,’ which are assemblies of internal commands stored as text files. Scripts can be called at any time on new patient files.
US2013197878A1 discloses a fluence and beam orientation optimization package for radiotherapy optimization, called PARETO (Pareto-Aware Radiotherapy Evolutionary Treatment Optimization), making use of a multi-objective genetic algorithm capable of optimizing several objective functions simultaneously and mapping the structure of their trade-off surface efficiently and in detail. PARETO generates a database of Pareto non-dominated solutions and allows the graphical exploration of trade-offs between multiple planning objectives during IMRT treatment planning PARETO offers automated multi-objective treatment plan optimization, which does not require any objective weights to be chosen, and therefore finds a large sample of optimized solutions defining a trade-off surface, which represents the range of compromises that are possible.
When invoked on a new patient, an auto-planning routine typically creates various target and normal tissue planning structures, sets up the beams and dose prescription, and loads customized intensity modulated radiation therapy (IMRT) objectives to start the optimization. Target objectives are based on the prescription dose, while organ at risk objectives are determined from a model that takes into account the geometric properties of the target and organs at risk to predict mean doses based on prior cases. Unfortunately, the time needed and quality achieved with auto-planning optimization is case dependent. Only rarely, the first auto-planned solution is clinically approved without further interaction. Rather, more frequently, additional manual parameter tweaking is required to meet as many clinical goals as possible. This additional refinement step of tweaking the parameters manually can take up to several hours, thereby diluting the benefits of auto-planning in the radiotherapy planning workflow.
The article “A DVH-guided IMRT optimization algorithm for automatic treatment planning and adaptive radiotherapy replanning” by M. Zarepisheh et al, Medical Physics, vol. 41, no. 6, page 061711 (2014) discloses an algorithm that automatically creates a treatment plan guided by the DVH curves of a reference plan that contains information on the clinician-approved dose-volume trade-offs among different targets/organs and among different portions of a DVH curve for an organ. In ART, the reference plan is the initial plan for the same patient, while for automatic treatment planning the reference plan is selected from a library of clinically approved and delivered plans of previously treated patients with similar medical conditions and geometry. The proposed algorithm employs a voxel-based optimization model and navigates the large voxel-based Pareto surface. The voxel weights are iteratively adjusted to approach a plan that is similar to the reference plan in terms of the DVHs. If the reference plan is feasible but not Pareto optimal, the algorithm generates a Pareto optimal plan with the DVHs better than the reference ones. If the reference plan is too restricting for the new geometry, the algorithm generates a Pareto plan with DVHs close to the reference ones. In both cases, the new plans have similar DVH trade-offs as the reference plans.